Overview

Brought to you by YData

Dataset statistics

Number of variables40
Number of observations1000
Missing cells1091
Missing cells (%)2.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.3 MiB
Average record size in memory1.3 KiB

Variable types

Numeric14
DateTime2
Categorical21
Text1
Boolean1
Unsupported1

Alerts

age is highly overall correlated with months_as_customerHigh correlation
auto_make is highly overall correlated with auto_modelHigh correlation
auto_model is highly overall correlated with auto_makeHigh correlation
collision_type is highly overall correlated with incident_type and 4 other fieldsHigh correlation
fraud_reported is highly overall correlated with incident_severityHigh correlation
incident_severity is highly overall correlated with fraud_reportedHigh correlation
incident_type is highly overall correlated with collision_type and 5 other fieldsHigh correlation
injury_claim is highly overall correlated with collision_type and 4 other fieldsHigh correlation
months_as_customer is highly overall correlated with ageHigh correlation
number_of_vehicles_involved is highly overall correlated with incident_typeHigh correlation
property_claim is highly overall correlated with collision_type and 4 other fieldsHigh correlation
total_claim_amount is highly overall correlated with collision_type and 4 other fieldsHigh correlation
vehicle_claim is highly overall correlated with collision_type and 4 other fieldsHigh correlation
authorities_contacted has 91 (9.1%) missing values Missing
_c39 has 1000 (100.0%) missing values Missing
policy_number has unique values Unique
incident_location has unique values Unique
_c39 is an unsupported type, check if it needs cleaning or further analysis Unsupported
umbrella_limit has 798 (79.8%) zeros Zeros
capital-gains has 508 (50.8%) zeros Zeros
capital-loss has 475 (47.5%) zeros Zeros
incident_hour_of_the_day has 52 (5.2%) zeros Zeros
injury_claim has 25 (2.5%) zeros Zeros
property_claim has 19 (1.9%) zeros Zeros

Reproduction

Analysis started2025-08-15 06:28:02.295250
Analysis finished2025-08-15 06:28:18.303731
Duration16.01 seconds
Software versionydata-profiling vv4.12.2
Download configurationconfig.json

Variables

months_as_customer
Real number (ℝ)

High correlation 

Distinct391
Distinct (%)39.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean203.954
Minimum0
Maximum479
Zeros1
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2025-08-15T13:28:18.369468image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile28.9
Q1115.75
median199.5
Q3276.25
95-th percentile429.05
Maximum479
Range479
Interquartile range (IQR)160.5

Descriptive statistics

Standard deviation115.11317
Coefficient of variation (CV)0.56440754
Kurtosis-0.48542807
Mean203.954
Median Absolute Deviation (MAD)80.5
Skewness0.36217685
Sum203954
Variance13251.043
MonotonicityNot monotonic
2025-08-15T13:28:18.426534image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
194 8
 
0.8%
285 7
 
0.7%
254 7
 
0.7%
101 7
 
0.7%
128 7
 
0.7%
230 7
 
0.7%
140 7
 
0.7%
210 7
 
0.7%
126 6
 
0.6%
289 6
 
0.6%
Other values (381) 931
93.1%
ValueCountFrequency (%)
0 1
 
0.1%
1 3
0.3%
2 2
0.2%
3 2
0.2%
4 3
0.3%
5 2
0.2%
6 1
 
0.1%
7 1
 
0.1%
8 3
0.3%
9 2
0.2%
ValueCountFrequency (%)
479 2
0.2%
478 2
0.2%
476 1
0.1%
475 2
0.2%
473 1
0.1%
472 1
0.1%
468 1
0.1%
467 1
0.1%
465 1
0.1%
464 1
0.1%

age
Real number (ℝ)

High correlation 

Distinct46
Distinct (%)4.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38.948
Minimum19
Maximum64
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2025-08-15T13:28:18.479726image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum19
5-th percentile26
Q132
median38
Q344
95-th percentile57
Maximum64
Range45
Interquartile range (IQR)12

Descriptive statistics

Standard deviation9.1402867
Coefficient of variation (CV)0.23467923
Kurtosis-0.26025502
Mean38.948
Median Absolute Deviation (MAD)6
Skewness0.47898805
Sum38948
Variance83.544841
MonotonicityNot monotonic
2025-08-15T13:28:18.530759image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
43 49
 
4.9%
39 48
 
4.8%
41 45
 
4.5%
34 44
 
4.4%
31 42
 
4.2%
38 42
 
4.2%
30 42
 
4.2%
37 41
 
4.1%
33 39
 
3.9%
40 38
 
3.8%
Other values (36) 570
57.0%
ValueCountFrequency (%)
19 1
 
0.1%
20 1
 
0.1%
21 6
 
0.6%
22 1
 
0.1%
23 7
 
0.7%
24 10
 
1.0%
25 14
1.4%
26 26
2.6%
27 24
2.4%
28 30
3.0%
ValueCountFrequency (%)
64 2
 
0.2%
63 2
 
0.2%
62 4
 
0.4%
61 10
1.0%
60 9
0.9%
59 5
 
0.5%
58 8
0.8%
57 16
1.6%
56 8
0.8%
55 14
1.4%

policy_number
Real number (ℝ)

Unique 

Distinct1000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean546238.65
Minimum100804
Maximum999435
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2025-08-15T13:28:18.580867image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum100804
5-th percentile143969.6
Q1335980.25
median533135
Q3759099.75
95-th percentile954279.1
Maximum999435
Range898631
Interquartile range (IQR)423119.5

Descriptive statistics

Standard deviation257063.01
Coefficient of variation (CV)0.47060567
Kurtosis-1.1326377
Mean546238.65
Median Absolute Deviation (MAD)210974
Skewness0.038990642
Sum5.4623865 × 108
Variance6.6081389 × 1010
MonotonicityNot monotonic
2025-08-15T13:28:18.638462image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
556080 1
 
0.1%
521585 1
 
0.1%
342868 1
 
0.1%
687698 1
 
0.1%
227811 1
 
0.1%
367455 1
 
0.1%
104594 1
 
0.1%
413978 1
 
0.1%
774895 1
 
0.1%
490514 1
 
0.1%
Other values (990) 990
99.0%
ValueCountFrequency (%)
100804 1
0.1%
101421 1
0.1%
104594 1
0.1%
106186 1
0.1%
106873 1
0.1%
107181 1
0.1%
108270 1
0.1%
108844 1
0.1%
109392 1
0.1%
110084 1
0.1%
ValueCountFrequency (%)
999435 1
0.1%
998865 1
0.1%
998192 1
0.1%
996850 1
0.1%
996253 1
0.1%
994538 1
0.1%
993840 1
0.1%
992145 1
0.1%
991553 1
0.1%
991480 1
0.1%
Distinct951
Distinct (%)95.1%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
Minimum1990-01-08 00:00:00
Maximum2015-02-22 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-08-15T13:28:18.693900image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-15T13:28:18.749789image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

policy_state
Categorical

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size49.9 KiB
OH
352 
IL
338 
IN
310 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters2000
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowOH
2nd rowIN
3rd rowOH
4th rowIL
5th rowIL

Common Values

ValueCountFrequency (%)
OH 352
35.2%
IL 338
33.8%
IN 310
31.0%

Length

2025-08-15T13:28:18.810552image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-15T13:28:18.845226image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
oh 352
35.2%
il 338
33.8%
in 310
31.0%

Most occurring characters

ValueCountFrequency (%)
I 648
32.4%
O 352
17.6%
H 352
17.6%
L 338
16.9%
N 310
15.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
I 648
32.4%
O 352
17.6%
H 352
17.6%
L 338
16.9%
N 310
15.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
I 648
32.4%
O 352
17.6%
H 352
17.6%
L 338
16.9%
N 310
15.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
I 648
32.4%
O 352
17.6%
H 352
17.6%
L 338
16.9%
N 310
15.5%

policy_csl
Categorical

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size55.1 KiB
250/500
351 
100/300
349 
500/1000
300 

Length

Max length8
Median length7
Mean length7.3
Min length7

Characters and Unicode

Total characters7300
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row250/500
2nd row250/500
3rd row100/300
4th row250/500
5th row500/1000

Common Values

ValueCountFrequency (%)
250/500 351
35.1%
100/300 349
34.9%
500/1000 300
30.0%

Length

2025-08-15T13:28:18.884050image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-15T13:28:18.918553image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
250/500 351
35.1%
100/300 349
34.9%
500/1000 300
30.0%

Most occurring characters

ValueCountFrequency (%)
0 3949
54.1%
5 1002
 
13.7%
/ 1000
 
13.7%
1 649
 
8.9%
2 351
 
4.8%
3 349
 
4.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7300
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 3949
54.1%
5 1002
 
13.7%
/ 1000
 
13.7%
1 649
 
8.9%
2 351
 
4.8%
3 349
 
4.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7300
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 3949
54.1%
5 1002
 
13.7%
/ 1000
 
13.7%
1 649
 
8.9%
2 351
 
4.8%
3 349
 
4.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7300
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 3949
54.1%
5 1002
 
13.7%
/ 1000
 
13.7%
1 649
 
8.9%
2 351
 
4.8%
3 349
 
4.8%
Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size51.6 KiB
1000
351 
500
342 
2000
307 

Length

Max length4
Median length4
Mean length3.658
Min length3

Characters and Unicode

Total characters3658
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1000
2nd row2000
3rd row2000
4th row2000
5th row1000

Common Values

ValueCountFrequency (%)
1000 351
35.1%
500 342
34.2%
2000 307
30.7%

Length

2025-08-15T13:28:18.960857image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-15T13:28:18.989697image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1000 351
35.1%
500 342
34.2%
2000 307
30.7%

Most occurring characters

ValueCountFrequency (%)
0 2658
72.7%
1 351
 
9.6%
5 342
 
9.3%
2 307
 
8.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3658
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 2658
72.7%
1 351
 
9.6%
5 342
 
9.3%
2 307
 
8.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3658
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 2658
72.7%
1 351
 
9.6%
5 342
 
9.3%
2 307
 
8.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3658
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 2658
72.7%
1 351
 
9.6%
5 342
 
9.3%
2 307
 
8.4%

policy_annual_premium
Real number (ℝ)

Distinct991
Distinct (%)99.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1256.4061
Minimum433.33
Maximum2047.59
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2025-08-15T13:28:19.029755image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum433.33
5-th percentile855.112
Q11089.6075
median1257.2
Q31415.695
95-th percentile1653.4435
Maximum2047.59
Range1614.26
Interquartile range (IQR)326.0875

Descriptive statistics

Standard deviation244.16739
Coefficient of variation (CV)0.19433795
Kurtosis0.07388944
Mean1256.4061
Median Absolute Deviation (MAD)164.26
Skewness0.0044019945
Sum1256406.1
Variance59617.717
MonotonicityNot monotonic
2025-08-15T13:28:19.088110image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1073.83 2
 
0.2%
1074.07 2
 
0.2%
1215.36 2
 
0.2%
1362.87 2
 
0.2%
1558.29 2
 
0.2%
1374.22 2
 
0.2%
1281.25 2
 
0.2%
1524.45 2
 
0.2%
1389.13 2
 
0.2%
1351.1 1
 
0.1%
Other values (981) 981
98.1%
ValueCountFrequency (%)
433.33 1
0.1%
484.67 1
0.1%
538.17 1
0.1%
566.11 1
0.1%
617.11 1
0.1%
625.08 1
0.1%
653.66 1
0.1%
664.86 1
0.1%
671.01 1
0.1%
671.92 1
0.1%
ValueCountFrequency (%)
2047.59 1
0.1%
1969.63 1
0.1%
1935.85 1
0.1%
1927.87 1
0.1%
1922.84 1
0.1%
1896.91 1
0.1%
1878.44 1
0.1%
1865.83 1
0.1%
1863.04 1
0.1%
1861.43 1
0.1%

umbrella_limit
Real number (ℝ)

Zeros 

Distinct11
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1101000
Minimum-1000000
Maximum10000000
Zeros798
Zeros (%)79.8%
Negative1
Negative (%)0.1%
Memory size7.9 KiB
2025-08-15T13:28:19.234192image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-1000000
5-th percentile0
Q10
median0
Q30
95-th percentile6000000
Maximum10000000
Range11000000
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2297406.6
Coefficient of variation (CV)2.0866545
Kurtosis1.7920773
Mean1101000
Median Absolute Deviation (MAD)0
Skewness1.8067122
Sum1.101 × 109
Variance5.2780771 × 1012
MonotonicityNot monotonic
2025-08-15T13:28:19.269463image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0 798
79.8%
6000000 57
 
5.7%
5000000 46
 
4.6%
4000000 39
 
3.9%
7000000 29
 
2.9%
3000000 12
 
1.2%
8000000 8
 
0.8%
9000000 5
 
0.5%
2000000 3
 
0.3%
10000000 2
 
0.2%
ValueCountFrequency (%)
-1000000 1
 
0.1%
0 798
79.8%
2000000 3
 
0.3%
3000000 12
 
1.2%
4000000 39
 
3.9%
5000000 46
 
4.6%
6000000 57
 
5.7%
7000000 29
 
2.9%
8000000 8
 
0.8%
9000000 5
 
0.5%
ValueCountFrequency (%)
10000000 2
 
0.2%
9000000 5
 
0.5%
8000000 8
 
0.8%
7000000 29
 
2.9%
6000000 57
 
5.7%
5000000 46
 
4.6%
4000000 39
 
3.9%
3000000 12
 
1.2%
2000000 3
 
0.3%
0 798
79.8%

insured_zip
Real number (ℝ)

Distinct995
Distinct (%)99.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean501214.49
Minimum430104
Maximum620962
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2025-08-15T13:28:19.310480image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum430104
5-th percentile433273.75
Q1448404.5
median466445.5
Q3603251
95-th percentile617463.35
Maximum620962
Range190858
Interquartile range (IQR)154846.5

Descriptive statistics

Standard deviation71701.611
Coefficient of variation (CV)0.14305574
Kurtosis-1.1907111
Mean501214.49
Median Absolute Deviation (MAD)21841
Skewness0.81655393
Sum5.0121449 × 108
Variance5.141121 × 109
MonotonicityNot monotonic
2025-08-15T13:28:19.373699image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
477695 2
 
0.2%
456602 2
 
0.2%
469429 2
 
0.2%
446895 2
 
0.2%
431202 2
 
0.2%
468176 1
 
0.1%
430632 1
 
0.1%
608117 1
 
0.1%
610706 1
 
0.1%
478456 1
 
0.1%
Other values (985) 985
98.5%
ValueCountFrequency (%)
430104 1
0.1%
430141 1
0.1%
430232 1
0.1%
430380 1
0.1%
430567 1
0.1%
430621 1
0.1%
430632 1
0.1%
430665 1
0.1%
430714 1
0.1%
430832 1
0.1%
ValueCountFrequency (%)
620962 1
0.1%
620869 1
0.1%
620819 1
0.1%
620757 1
0.1%
620737 1
0.1%
620507 1
0.1%
620493 1
0.1%
620473 1
0.1%
620358 1
0.1%
620207 1
0.1%

insured_sex
Categorical

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size52.9 KiB
FEMALE
537 
MALE
463 

Length

Max length6
Median length6
Mean length5.074
Min length4

Characters and Unicode

Total characters5074
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMALE
2nd rowMALE
3rd rowFEMALE
4th rowFEMALE
5th rowMALE

Common Values

ValueCountFrequency (%)
FEMALE 537
53.7%
MALE 463
46.3%

Length

2025-08-15T13:28:19.429693image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-15T13:28:19.460206image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
female 537
53.7%
male 463
46.3%

Most occurring characters

ValueCountFrequency (%)
E 1537
30.3%
A 1000
19.7%
M 1000
19.7%
L 1000
19.7%
F 537
 
10.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5074
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
E 1537
30.3%
A 1000
19.7%
M 1000
19.7%
L 1000
19.7%
F 537
 
10.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5074
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
E 1537
30.3%
A 1000
19.7%
M 1000
19.7%
L 1000
19.7%
F 537
 
10.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5074
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
E 1537
30.3%
A 1000
19.7%
M 1000
19.7%
L 1000
19.7%
F 537
 
10.6%
Distinct7
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size53.7 KiB
JD
161 
High School
160 
Associate
145 
MD
144 
Masters
143 
Other values (2)
247 

Length

Max length11
Median length9
Mean length5.905
Min length2

Characters and Unicode

Total characters5905
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMD
2nd rowMD
3rd rowPhD
4th rowPhD
5th rowAssociate

Common Values

ValueCountFrequency (%)
JD 161
16.1%
High School 160
16.0%
Associate 145
14.5%
MD 144
14.4%
Masters 143
14.3%
PhD 125
12.5%
College 122
12.2%

Length

2025-08-15T13:28:19.491275image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-15T13:28:19.528668image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
jd 161
13.9%
high 160
13.8%
school 160
13.8%
associate 145
12.5%
md 144
12.4%
masters 143
12.3%
phd 125
10.8%
college 122
10.5%

Most occurring characters

ValueCountFrequency (%)
o 587
 
9.9%
s 576
 
9.8%
e 532
 
9.0%
h 445
 
7.5%
D 430
 
7.3%
l 404
 
6.8%
i 305
 
5.2%
c 305
 
5.2%
t 288
 
4.9%
a 288
 
4.9%
Other values (10) 1745
29.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5905
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 587
 
9.9%
s 576
 
9.8%
e 532
 
9.0%
h 445
 
7.5%
D 430
 
7.3%
l 404
 
6.8%
i 305
 
5.2%
c 305
 
5.2%
t 288
 
4.9%
a 288
 
4.9%
Other values (10) 1745
29.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5905
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 587
 
9.9%
s 576
 
9.8%
e 532
 
9.0%
h 445
 
7.5%
D 430
 
7.3%
l 404
 
6.8%
i 305
 
5.2%
c 305
 
5.2%
t 288
 
4.9%
a 288
 
4.9%
Other values (10) 1745
29.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5905
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 587
 
9.9%
s 576
 
9.8%
e 532
 
9.0%
h 445
 
7.5%
D 430
 
7.3%
l 404
 
6.8%
i 305
 
5.2%
c 305
 
5.2%
t 288
 
4.9%
a 288
 
4.9%
Other values (10) 1745
29.6%
Distinct14
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size61.2 KiB
machine-op-inspct
93 
prof-specialty
85 
tech-support
78 
sales
76 
exec-managerial
76 
Other values (9)
592 

Length

Max length17
Median length16
Mean length13.521
Min length5

Characters and Unicode

Total characters13521
Distinct characters21
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowcraft-repair
2nd rowmachine-op-inspct
3rd rowsales
4th rowarmed-forces
5th rowsales

Common Values

ValueCountFrequency (%)
machine-op-inspct 93
 
9.3%
prof-specialty 85
 
8.5%
tech-support 78
 
7.8%
sales 76
 
7.6%
exec-managerial 76
 
7.6%
craft-repair 74
 
7.4%
transport-moving 72
 
7.2%
priv-house-serv 71
 
7.1%
other-service 71
 
7.1%
armed-forces 69
 
6.9%
Other values (4) 235
23.5%

Length

2025-08-15T13:28:19.581505image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
machine-op-inspct 93
 
9.3%
prof-specialty 85
 
8.5%
tech-support 78
 
7.8%
sales 76
 
7.6%
exec-managerial 76
 
7.6%
craft-repair 74
 
7.4%
transport-moving 72
 
7.2%
priv-house-serv 71
 
7.1%
other-service 71
 
7.1%
armed-forces 69
 
6.9%
Other values (4) 235
23.5%

Most occurring characters

ValueCountFrequency (%)
e 1543
11.4%
r 1379
10.2%
- 1088
 
8.0%
a 1062
 
7.9%
s 986
 
7.3%
i 922
 
6.8%
c 886
 
6.6%
p 792
 
5.9%
t 749
 
5.5%
o 674
 
5.0%
Other values (11) 3440
25.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 13521
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 1543
11.4%
r 1379
10.2%
- 1088
 
8.0%
a 1062
 
7.9%
s 986
 
7.3%
i 922
 
6.8%
c 886
 
6.6%
p 792
 
5.9%
t 749
 
5.5%
o 674
 
5.0%
Other values (11) 3440
25.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 13521
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 1543
11.4%
r 1379
10.2%
- 1088
 
8.0%
a 1062
 
7.9%
s 986
 
7.3%
i 922
 
6.8%
c 886
 
6.6%
p 792
 
5.9%
t 749
 
5.5%
o 674
 
5.0%
Other values (11) 3440
25.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 13521
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 1543
11.4%
r 1379
10.2%
- 1088
 
8.0%
a 1062
 
7.9%
s 986
 
7.3%
i 922
 
6.8%
c 886
 
6.6%
p 792
 
5.9%
t 749
 
5.5%
o 674
 
5.0%
Other values (11) 3440
25.4%

insured_hobbies
Categorical

Distinct20
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size55.9 KiB
reading
 
64
exercise
 
57
paintball
 
57
bungie-jumping
 
56
movies
 
55
Other values (15)
711 

Length

Max length14
Median length11
Mean length8.113
Min length4

Characters and Unicode

Total characters8113
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowsleeping
2nd rowreading
3rd rowboard-games
4th rowboard-games
5th rowboard-games

Common Values

ValueCountFrequency (%)
reading 64
 
6.4%
exercise 57
 
5.7%
paintball 57
 
5.7%
bungie-jumping 56
 
5.6%
movies 55
 
5.5%
camping 55
 
5.5%
golf 55
 
5.5%
kayaking 54
 
5.4%
yachting 53
 
5.3%
hiking 52
 
5.2%
Other values (10) 442
44.2%

Length

2025-08-15T13:28:19.629736image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
reading 64
 
6.4%
exercise 57
 
5.7%
paintball 57
 
5.7%
bungie-jumping 56
 
5.6%
movies 55
 
5.5%
camping 55
 
5.5%
golf 55
 
5.5%
kayaking 54
 
5.4%
yachting 53
 
5.3%
hiking 52
 
5.2%
Other values (10) 442
44.2%

Most occurring characters

ValueCountFrequency (%)
i 927
 
11.4%
g 725
 
8.9%
e 705
 
8.7%
a 700
 
8.6%
n 672
 
8.3%
s 545
 
6.7%
o 337
 
4.2%
l 325
 
4.0%
m 313
 
3.9%
p 305
 
3.8%
Other values (14) 2559
31.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8113
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 927
 
11.4%
g 725
 
8.9%
e 705
 
8.7%
a 700
 
8.6%
n 672
 
8.3%
s 545
 
6.7%
o 337
 
4.2%
l 325
 
4.0%
m 313
 
3.9%
p 305
 
3.8%
Other values (14) 2559
31.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8113
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 927
 
11.4%
g 725
 
8.9%
e 705
 
8.7%
a 700
 
8.6%
n 672
 
8.3%
s 545
 
6.7%
o 337
 
4.2%
l 325
 
4.0%
m 313
 
3.9%
p 305
 
3.8%
Other values (14) 2559
31.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8113
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 927
 
11.4%
g 725
 
8.9%
e 705
 
8.7%
a 700
 
8.6%
n 672
 
8.3%
s 545
 
6.7%
o 337
 
4.2%
l 325
 
4.0%
m 313
 
3.9%
p 305
 
3.8%
Other values (14) 2559
31.5%
Distinct6
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size57.2 KiB
own-child
183 
other-relative
177 
not-in-family
174 
husband
170 
wife
155 

Length

Max length14
Median length13
Mean length9.466
Min length4

Characters and Unicode

Total characters9466
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowhusband
2nd rowother-relative
3rd rowown-child
4th rowunmarried
5th rowunmarried

Common Values

ValueCountFrequency (%)
own-child 183
18.3%
other-relative 177
17.7%
not-in-family 174
17.4%
husband 170
17.0%
wife 155
15.5%
unmarried 141
14.1%

Length

2025-08-15T13:28:19.671566image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-15T13:28:19.709372image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
own-child 183
18.3%
other-relative 177
17.7%
not-in-family 174
17.4%
husband 170
17.0%
wife 155
15.5%
unmarried 141
14.1%

Most occurring characters

ValueCountFrequency (%)
i 1004
 
10.6%
n 842
 
8.9%
e 827
 
8.7%
- 708
 
7.5%
a 662
 
7.0%
r 636
 
6.7%
l 534
 
5.6%
o 534
 
5.6%
h 530
 
5.6%
t 528
 
5.6%
Other values (10) 2661
28.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 9466
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 1004
 
10.6%
n 842
 
8.9%
e 827
 
8.7%
- 708
 
7.5%
a 662
 
7.0%
r 636
 
6.7%
l 534
 
5.6%
o 534
 
5.6%
h 530
 
5.6%
t 528
 
5.6%
Other values (10) 2661
28.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 9466
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 1004
 
10.6%
n 842
 
8.9%
e 827
 
8.7%
- 708
 
7.5%
a 662
 
7.0%
r 636
 
6.7%
l 534
 
5.6%
o 534
 
5.6%
h 530
 
5.6%
t 528
 
5.6%
Other values (10) 2661
28.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 9466
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 1004
 
10.6%
n 842
 
8.9%
e 827
 
8.7%
- 708
 
7.5%
a 662
 
7.0%
r 636
 
6.7%
l 534
 
5.6%
o 534
 
5.6%
h 530
 
5.6%
t 528
 
5.6%
Other values (10) 2661
28.1%

capital-gains
Real number (ℝ)

Zeros 

Distinct338
Distinct (%)33.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25126.1
Minimum0
Maximum100500
Zeros508
Zeros (%)50.8%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2025-08-15T13:28:19.767955image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q351025
95-th percentile70615
Maximum100500
Range100500
Interquartile range (IQR)51025

Descriptive statistics

Standard deviation27872.188
Coefficient of variation (CV)1.1092922
Kurtosis-1.2767035
Mean25126.1
Median Absolute Deviation (MAD)0
Skewness0.47885023
Sum25126100
Variance7.7685885 × 108
MonotonicityNot monotonic
2025-08-15T13:28:19.823346image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 508
50.8%
46300 5
 
0.5%
68500 4
 
0.4%
51500 4
 
0.4%
67800 3
 
0.3%
35100 3
 
0.3%
51400 3
 
0.3%
49700 3
 
0.3%
37900 3
 
0.3%
38600 3
 
0.3%
Other values (328) 461
46.1%
ValueCountFrequency (%)
0 508
50.8%
800 1
 
0.1%
10000 1
 
0.1%
11000 1
 
0.1%
12100 1
 
0.1%
12800 1
 
0.1%
13100 1
 
0.1%
14100 1
 
0.1%
16100 1
 
0.1%
17300 1
 
0.1%
ValueCountFrequency (%)
100500 1
0.1%
98800 1
0.1%
94800 1
0.1%
91900 1
0.1%
90700 1
0.1%
88800 1
0.1%
88400 1
0.1%
87800 1
0.1%
84900 1
0.1%
83900 1
0.1%

capital-loss
Real number (ℝ)

Zeros 

Distinct354
Distinct (%)35.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-26793.7
Minimum-111100
Maximum0
Zeros475
Zeros (%)47.5%
Negative525
Negative (%)52.5%
Memory size7.9 KiB
2025-08-15T13:28:19.872790image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-111100
5-th percentile-72305
Q1-51500
median-23250
Q30
95-th percentile0
Maximum0
Range111100
Interquartile range (IQR)51500

Descriptive statistics

Standard deviation28104.097
Coefficient of variation (CV)-1.0489069
Kurtosis-1.3138745
Mean-26793.7
Median Absolute Deviation (MAD)23250
Skewness-0.39147194
Sum-26793700
Variance7.8984025 × 108
MonotonicityNot monotonic
2025-08-15T13:28:19.929415image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 475
47.5%
-53700 5
 
0.5%
-50300 5
 
0.5%
-31700 5
 
0.5%
-61400 4
 
0.4%
-31400 4
 
0.4%
-45300 4
 
0.4%
-49200 4
 
0.4%
-53800 4
 
0.4%
-32800 4
 
0.4%
Other values (344) 486
48.6%
ValueCountFrequency (%)
-111100 1
0.1%
-93600 1
0.1%
-91400 1
0.1%
-91200 1
0.1%
-90600 1
0.1%
-90200 1
0.1%
-90100 1
0.1%
-89400 1
0.1%
-88300 1
0.1%
-87300 1
0.1%
ValueCountFrequency (%)
0 475
47.5%
-5700 1
 
0.1%
-6300 1
 
0.1%
-8500 1
 
0.1%
-10600 1
 
0.1%
-12100 1
 
0.1%
-13200 1
 
0.1%
-13800 1
 
0.1%
-15600 1
 
0.1%
-15700 2
 
0.2%
Distinct60
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
Minimum2015-01-01 00:00:00
Maximum2015-03-01 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-08-15T13:28:19.989001image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-15T13:28:20.048947image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

incident_type
Categorical

High correlation 

Distinct4
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size68.9 KiB
Multi-vehicle Collision
419 
Single Vehicle Collision
403 
Vehicle Theft
94 
Parked Car
84 

Length

Max length24
Median length23
Mean length21.371
Min length10

Characters and Unicode

Total characters21371
Distinct characters25
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSingle Vehicle Collision
2nd rowVehicle Theft
3rd rowMulti-vehicle Collision
4th rowSingle Vehicle Collision
5th rowVehicle Theft

Common Values

ValueCountFrequency (%)
Multi-vehicle Collision 419
41.9%
Single Vehicle Collision 403
40.3%
Vehicle Theft 94
 
9.4%
Parked Car 84
 
8.4%

Length

2025-08-15T13:28:20.101821image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-15T13:28:20.130278image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
collision 822
34.2%
vehicle 497
20.7%
multi-vehicle 419
17.4%
single 403
16.8%
theft 94
 
3.9%
parked 84
 
3.5%
car 84
 
3.5%

Most occurring characters

ValueCountFrequency (%)
l 3382
15.8%
i 3382
15.8%
e 2413
11.3%
o 1644
 
7.7%
1403
 
6.6%
n 1225
 
5.7%
h 1010
 
4.7%
c 916
 
4.3%
C 906
 
4.2%
s 822
 
3.8%
Other values (15) 4268
20.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 21371
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
l 3382
15.8%
i 3382
15.8%
e 2413
11.3%
o 1644
 
7.7%
1403
 
6.6%
n 1225
 
5.7%
h 1010
 
4.7%
c 916
 
4.3%
C 906
 
4.2%
s 822
 
3.8%
Other values (15) 4268
20.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 21371
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
l 3382
15.8%
i 3382
15.8%
e 2413
11.3%
o 1644
 
7.7%
1403
 
6.6%
n 1225
 
5.7%
h 1010
 
4.7%
c 916
 
4.3%
C 906
 
4.2%
s 822
 
3.8%
Other values (15) 4268
20.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 21371
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
l 3382
15.8%
i 3382
15.8%
e 2413
11.3%
o 1644
 
7.7%
1403
 
6.6%
n 1225
 
5.7%
h 1010
 
4.7%
c 916
 
4.3%
C 906
 
4.2%
s 822
 
3.8%
Other values (15) 4268
20.0%

collision_type
Categorical

High correlation 

Distinct4
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size59.6 KiB
Rear Collision
292 
Side Collision
276 
Front Collision
254 
?
178 

Length

Max length15
Median length14
Mean length11.94
Min length1

Characters and Unicode

Total characters11940
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSide Collision
2nd row?
3rd rowRear Collision
4th rowFront Collision
5th row?

Common Values

ValueCountFrequency (%)
Rear Collision 292
29.2%
Side Collision 276
27.6%
Front Collision 254
25.4%
? 178
17.8%

Length

2025-08-15T13:28:20.179783image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-15T13:28:20.212178image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
collision 822
45.1%
rear 292
 
16.0%
side 276
 
15.1%
front 254
 
13.9%
178
 
9.8%

Most occurring characters

ValueCountFrequency (%)
i 1920
16.1%
o 1898
15.9%
l 1644
13.8%
n 1076
9.0%
822
6.9%
s 822
6.9%
C 822
6.9%
e 568
 
4.8%
r 546
 
4.6%
a 292
 
2.4%
Other values (6) 1530
12.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 11940
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 1920
16.1%
o 1898
15.9%
l 1644
13.8%
n 1076
9.0%
822
6.9%
s 822
6.9%
C 822
6.9%
e 568
 
4.8%
r 546
 
4.6%
a 292
 
2.4%
Other values (6) 1530
12.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 11940
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 1920
16.1%
o 1898
15.9%
l 1644
13.8%
n 1076
9.0%
822
6.9%
s 822
6.9%
C 822
6.9%
e 568
 
4.8%
r 546
 
4.6%
a 292
 
2.4%
Other values (6) 1530
12.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 11940
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 1920
16.1%
o 1898
15.9%
l 1644
13.8%
n 1076
9.0%
822
6.9%
s 822
6.9%
C 822
6.9%
e 568
 
4.8%
r 546
 
4.6%
a 292
 
2.4%
Other values (6) 1530
12.8%

incident_severity
Categorical

High correlation 

Distinct4
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size59.3 KiB
Minor Damage
354 
Total Loss
280 
Major Damage
276 
Trivial Damage
90 

Length

Max length14
Median length12
Mean length11.62
Min length10

Characters and Unicode

Total characters11620
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMajor Damage
2nd rowMinor Damage
3rd rowMinor Damage
4th rowMajor Damage
5th rowMinor Damage

Common Values

ValueCountFrequency (%)
Minor Damage 354
35.4%
Total Loss 280
28.0%
Major Damage 276
27.6%
Trivial Damage 90
 
9.0%

Length

2025-08-15T13:28:20.261326image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-15T13:28:20.300406image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
damage 720
36.0%
minor 354
17.7%
total 280
 
14.0%
loss 280
 
14.0%
major 276
 
13.8%
trivial 90
 
4.5%

Most occurring characters

ValueCountFrequency (%)
a 2086
18.0%
o 1190
10.2%
1000
 
8.6%
D 720
 
6.2%
m 720
 
6.2%
r 720
 
6.2%
g 720
 
6.2%
e 720
 
6.2%
M 630
 
5.4%
s 560
 
4.8%
Other values (8) 2554
22.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 11620
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 2086
18.0%
o 1190
10.2%
1000
 
8.6%
D 720
 
6.2%
m 720
 
6.2%
r 720
 
6.2%
g 720
 
6.2%
e 720
 
6.2%
M 630
 
5.4%
s 560
 
4.8%
Other values (8) 2554
22.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 11620
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 2086
18.0%
o 1190
10.2%
1000
 
8.6%
D 720
 
6.2%
m 720
 
6.2%
r 720
 
6.2%
g 720
 
6.2%
e 720
 
6.2%
M 630
 
5.4%
s 560
 
4.8%
Other values (8) 2554
22.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 11620
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 2086
18.0%
o 1190
10.2%
1000
 
8.6%
D 720
 
6.2%
m 720
 
6.2%
r 720
 
6.2%
g 720
 
6.2%
e 720
 
6.2%
M 630
 
5.4%
s 560
 
4.8%
Other values (8) 2554
22.0%

authorities_contacted
Categorical

Missing 

Distinct4
Distinct (%)0.4%
Missing91
Missing (%)9.1%
Memory size53.9 KiB
Police
292 
Fire
223 
Other
198 
Ambulance
196 

Length

Max length9
Median length6
Mean length5.9383938
Min length4

Characters and Unicode

Total characters5398
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPolice
2nd rowPolice
3rd rowPolice
4th rowPolice
5th rowFire

Common Values

ValueCountFrequency (%)
Police 292
29.2%
Fire 223
22.3%
Other 198
19.8%
Ambulance 196
19.6%
(Missing) 91
 
9.1%

Length

2025-08-15T13:28:20.344155image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-15T13:28:20.379606image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
police 292
32.1%
fire 223
24.5%
other 198
21.8%
ambulance 196
21.6%

Most occurring characters

ValueCountFrequency (%)
e 909
16.8%
i 515
 
9.5%
c 488
 
9.0%
l 488
 
9.0%
r 421
 
7.8%
P 292
 
5.4%
o 292
 
5.4%
F 223
 
4.1%
O 198
 
3.7%
t 198
 
3.7%
Other values (7) 1374
25.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5398
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 909
16.8%
i 515
 
9.5%
c 488
 
9.0%
l 488
 
9.0%
r 421
 
7.8%
P 292
 
5.4%
o 292
 
5.4%
F 223
 
4.1%
O 198
 
3.7%
t 198
 
3.7%
Other values (7) 1374
25.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5398
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 909
16.8%
i 515
 
9.5%
c 488
 
9.0%
l 488
 
9.0%
r 421
 
7.8%
P 292
 
5.4%
o 292
 
5.4%
F 223
 
4.1%
O 198
 
3.7%
t 198
 
3.7%
Other values (7) 1374
25.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5398
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 909
16.8%
i 515
 
9.5%
c 488
 
9.0%
l 488
 
9.0%
r 421
 
7.8%
P 292
 
5.4%
o 292
 
5.4%
F 223
 
4.1%
O 198
 
3.7%
t 198
 
3.7%
Other values (7) 1374
25.5%

incident_state
Categorical

Distinct7
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size49.9 KiB
NY
262 
SC
248 
WV
217 
VA
110 
NC
110 
Other values (2)
53 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters2000
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSC
2nd rowVA
3rd rowNY
4th rowOH
5th rowNY

Common Values

ValueCountFrequency (%)
NY 262
26.2%
SC 248
24.8%
WV 217
21.7%
VA 110
11.0%
NC 110
11.0%
PA 30
 
3.0%
OH 23
 
2.3%

Length

2025-08-15T13:28:20.420574image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-15T13:28:20.457528image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
ny 262
26.2%
sc 248
24.8%
wv 217
21.7%
va 110
11.0%
nc 110
11.0%
pa 30
 
3.0%
oh 23
 
2.3%

Most occurring characters

ValueCountFrequency (%)
N 372
18.6%
C 358
17.9%
V 327
16.4%
Y 262
13.1%
S 248
12.4%
W 217
10.8%
A 140
 
7.0%
P 30
 
1.5%
O 23
 
1.1%
H 23
 
1.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 372
18.6%
C 358
17.9%
V 327
16.4%
Y 262
13.1%
S 248
12.4%
W 217
10.8%
A 140
 
7.0%
P 30
 
1.5%
O 23
 
1.1%
H 23
 
1.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 372
18.6%
C 358
17.9%
V 327
16.4%
Y 262
13.1%
S 248
12.4%
W 217
10.8%
A 140
 
7.0%
P 30
 
1.5%
O 23
 
1.1%
H 23
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 372
18.6%
C 358
17.9%
V 327
16.4%
Y 262
13.1%
S 248
12.4%
W 217
10.8%
A 140
 
7.0%
P 30
 
1.5%
O 23
 
1.1%
H 23
 
1.1%

incident_city
Categorical

Distinct7
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size57.0 KiB
Springfield
157 
Arlington
152 
Columbus
149 
Northbend
145 
Hillsdale
141 
Other values (2)
256 

Length

Max length11
Median length9
Mean length9.287
Min length8

Characters and Unicode

Total characters9287
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowColumbus
2nd rowRiverwood
3rd rowColumbus
4th rowArlington
5th rowArlington

Common Values

ValueCountFrequency (%)
Springfield 157
15.7%
Arlington 152
15.2%
Columbus 149
14.9%
Northbend 145
14.5%
Hillsdale 141
14.1%
Riverwood 134
13.4%
Northbrook 122
12.2%

Length

2025-08-15T13:28:20.511610image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-15T13:28:20.549560image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
springfield 157
15.7%
arlington 152
15.2%
columbus 149
14.9%
northbend 145
14.5%
hillsdale 141
14.1%
riverwood 134
13.4%
northbrook 122
12.2%

Most occurring characters

ValueCountFrequency (%)
o 1080
 
11.6%
l 881
 
9.5%
r 832
 
9.0%
i 741
 
8.0%
n 606
 
6.5%
d 577
 
6.2%
e 577
 
6.2%
t 419
 
4.5%
b 416
 
4.5%
g 309
 
3.3%
Other values (16) 2849
30.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 9287
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 1080
 
11.6%
l 881
 
9.5%
r 832
 
9.0%
i 741
 
8.0%
n 606
 
6.5%
d 577
 
6.2%
e 577
 
6.2%
t 419
 
4.5%
b 416
 
4.5%
g 309
 
3.3%
Other values (16) 2849
30.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 9287
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 1080
 
11.6%
l 881
 
9.5%
r 832
 
9.0%
i 741
 
8.0%
n 606
 
6.5%
d 577
 
6.2%
e 577
 
6.2%
t 419
 
4.5%
b 416
 
4.5%
g 309
 
3.3%
Other values (16) 2849
30.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 9287
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 1080
 
11.6%
l 881
 
9.5%
r 832
 
9.0%
i 741
 
8.0%
n 606
 
6.5%
d 577
 
6.2%
e 577
 
6.2%
t 419
 
4.5%
b 416
 
4.5%
g 309
 
3.3%
Other values (16) 2849
30.7%

incident_location
Text

Unique 

Distinct1000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size62.4 KiB
2025-08-15T13:28:20.669675image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length23
Median length20
Mean length14.749
Min length11

Characters and Unicode

Total characters14749
Distinct characters49
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1000 ?
Unique (%)100.0%

Sample

1st row9935 4th Drive
2nd row6608 MLK Hwy
3rd row7121 Francis Lane
4th row6956 Maple Drive
5th row3041 3rd Ave
ValueCountFrequency (%)
drive 173
 
5.8%
lane 171
 
5.7%
ridge 171
 
5.7%
st 171
 
5.7%
ave 161
 
5.4%
hwy 153
 
5.1%
4th 57
 
1.9%
5th 52
 
1.7%
texas 47
 
1.6%
mlk 45
 
1.5%
Other values (961) 1799
60.0%
2025-08-15T13:28:20.802409image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2000
 
13.6%
e 1236
 
8.4%
i 629
 
4.3%
a 603
 
4.1%
n 518
 
3.5%
r 491
 
3.3%
t 474
 
3.2%
5 467
 
3.2%
1 443
 
3.0%
4 441
 
3.0%
Other values (39) 7447
50.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 14749
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2000
 
13.6%
e 1236
 
8.4%
i 629
 
4.3%
a 603
 
4.1%
n 518
 
3.5%
r 491
 
3.3%
t 474
 
3.2%
5 467
 
3.2%
1 443
 
3.0%
4 441
 
3.0%
Other values (39) 7447
50.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 14749
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2000
 
13.6%
e 1236
 
8.4%
i 629
 
4.3%
a 603
 
4.1%
n 518
 
3.5%
r 491
 
3.3%
t 474
 
3.2%
5 467
 
3.2%
1 443
 
3.0%
4 441
 
3.0%
Other values (39) 7447
50.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 14749
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2000
 
13.6%
e 1236
 
8.4%
i 629
 
4.3%
a 603
 
4.1%
n 518
 
3.5%
r 491
 
3.3%
t 474
 
3.2%
5 467
 
3.2%
1 443
 
3.0%
4 441
 
3.0%
Other values (39) 7447
50.5%

incident_hour_of_the_day
Real number (ℝ)

Zeros 

Distinct24
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.644
Minimum0
Maximum23
Zeros52
Zeros (%)5.2%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2025-08-15T13:28:20.845269image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q16
median12
Q317
95-th percentile23
Maximum23
Range23
Interquartile range (IQR)11

Descriptive statistics

Standard deviation6.9513729
Coefficient of variation (CV)0.59699184
Kurtosis-1.1929402
Mean11.644
Median Absolute Deviation (MAD)6
Skewness-0.035584466
Sum11644
Variance48.321586
MonotonicityNot monotonic
2025-08-15T13:28:20.880682image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
17 54
 
5.4%
3 53
 
5.3%
0 52
 
5.2%
23 51
 
5.1%
16 49
 
4.9%
4 46
 
4.6%
10 46
 
4.6%
13 46
 
4.6%
6 44
 
4.4%
14 43
 
4.3%
Other values (14) 516
51.6%
ValueCountFrequency (%)
0 52
5.2%
1 29
2.9%
2 31
3.1%
3 53
5.3%
4 46
4.6%
5 33
3.3%
6 44
4.4%
7 40
4.0%
8 36
3.6%
9 43
4.3%
ValueCountFrequency (%)
23 51
5.1%
22 38
3.8%
21 42
4.2%
20 34
3.4%
19 40
4.0%
18 41
4.1%
17 54
5.4%
16 49
4.9%
15 39
3.9%
14 43
4.3%

number_of_vehicles_involved
Categorical

High correlation 

Distinct4
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size49.0 KiB
1
581 
3
358 
4
 
31
2
 
30

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1000
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row3
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 581
58.1%
3 358
35.8%
4 31
 
3.1%
2 30
 
3.0%

Length

2025-08-15T13:28:20.920671image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-15T13:28:20.957027image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1 581
58.1%
3 358
35.8%
4 31
 
3.1%
2 30
 
3.0%

Most occurring characters

ValueCountFrequency (%)
1 581
58.1%
3 358
35.8%
4 31
 
3.1%
2 30
 
3.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 581
58.1%
3 358
35.8%
4 31
 
3.1%
2 30
 
3.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 581
58.1%
3 358
35.8%
4 31
 
3.1%
2 30
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 581
58.1%
3 358
35.8%
4 31
 
3.1%
2 30
 
3.0%

property_damage
Categorical

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size49.9 KiB
?
360 
NO
338 
YES
302 

Length

Max length3
Median length2
Mean length1.942
Min length1

Characters and Unicode

Total characters1942
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowYES
2nd row?
3rd rowNO
4th row?
5th rowNO

Common Values

ValueCountFrequency (%)
? 360
36.0%
NO 338
33.8%
YES 302
30.2%

Length

2025-08-15T13:28:21.110787image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-15T13:28:21.143911image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
360
36.0%
no 338
33.8%
yes 302
30.2%

Most occurring characters

ValueCountFrequency (%)
? 360
18.5%
N 338
17.4%
O 338
17.4%
Y 302
15.6%
E 302
15.6%
S 302
15.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1942
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
? 360
18.5%
N 338
17.4%
O 338
17.4%
Y 302
15.6%
E 302
15.6%
S 302
15.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1942
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
? 360
18.5%
N 338
17.4%
O 338
17.4%
Y 302
15.6%
E 302
15.6%
S 302
15.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1942
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
? 360
18.5%
N 338
17.4%
O 338
17.4%
Y 302
15.6%
E 302
15.6%
S 302
15.6%

bodily_injuries
Categorical

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size49.0 KiB
0
340 
2
332 
1
328 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1000
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row2
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 340
34.0%
2 332
33.2%
1 328
32.8%

Length

2025-08-15T13:28:21.179471image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-15T13:28:21.205098image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 340
34.0%
2 332
33.2%
1 328
32.8%

Most occurring characters

ValueCountFrequency (%)
0 340
34.0%
2 332
33.2%
1 328
32.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 340
34.0%
2 332
33.2%
1 328
32.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 340
34.0%
2 332
33.2%
1 328
32.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 340
34.0%
2 332
33.2%
1 328
32.8%

witnesses
Categorical

Distinct4
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size49.0 KiB
1
258 
2
250 
0
249 
3
243 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1000
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row0
3rd row3
4th row2
5th row1

Common Values

ValueCountFrequency (%)
1 258
25.8%
2 250
25.0%
0 249
24.9%
3 243
24.3%

Length

2025-08-15T13:28:21.241897image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-15T13:28:21.269783image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1 258
25.8%
2 250
25.0%
0 249
24.9%
3 243
24.3%

Most occurring characters

ValueCountFrequency (%)
1 258
25.8%
2 250
25.0%
0 249
24.9%
3 243
24.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 258
25.8%
2 250
25.0%
0 249
24.9%
3 243
24.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 258
25.8%
2 250
25.0%
0 249
24.9%
3 243
24.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 258
25.8%
2 250
25.0%
0 249
24.9%
3 243
24.3%
Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size49.9 KiB
?
343 
NO
343 
YES
314 

Length

Max length3
Median length2
Mean length1.971
Min length1

Characters and Unicode

Total characters1971
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowYES
2nd row?
3rd rowNO
4th rowNO
5th rowNO

Common Values

ValueCountFrequency (%)
? 343
34.3%
NO 343
34.3%
YES 314
31.4%

Length

2025-08-15T13:28:21.320666image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-15T13:28:21.349777image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
343
34.3%
no 343
34.3%
yes 314
31.4%

Most occurring characters

ValueCountFrequency (%)
? 343
17.4%
N 343
17.4%
O 343
17.4%
Y 314
15.9%
E 314
15.9%
S 314
15.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1971
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
? 343
17.4%
N 343
17.4%
O 343
17.4%
Y 314
15.9%
E 314
15.9%
S 314
15.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1971
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
? 343
17.4%
N 343
17.4%
O 343
17.4%
Y 314
15.9%
E 314
15.9%
S 314
15.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1971
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
? 343
17.4%
N 343
17.4%
O 343
17.4%
Y 314
15.9%
E 314
15.9%
S 314
15.9%

total_claim_amount
Real number (ℝ)

High correlation 

Distinct763
Distinct (%)76.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean52761.94
Minimum100
Maximum114920
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2025-08-15T13:28:21.391263image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum100
5-th percentile4320
Q141812.5
median58055
Q370592.5
95-th percentile88413
Maximum114920
Range114820
Interquartile range (IQR)28780

Descriptive statistics

Standard deviation26401.533
Coefficient of variation (CV)0.50038974
Kurtosis-0.45408143
Mean52761.94
Median Absolute Deviation (MAD)13855
Skewness-0.59458199
Sum52761940
Variance6.9704095 × 108
MonotonicityNot monotonic
2025-08-15T13:28:21.450692image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
59400 5
 
0.5%
44200 4
 
0.4%
3190 4
 
0.4%
75400 4
 
0.4%
70400 4
 
0.4%
60600 4
 
0.4%
4320 4
 
0.4%
70290 4
 
0.4%
5940 4
 
0.4%
58500 4
 
0.4%
Other values (753) 959
95.9%
ValueCountFrequency (%)
100 1
 
0.1%
1920 1
 
0.1%
2160 1
 
0.1%
2250 1
 
0.1%
2400 1
 
0.1%
2520 1
 
0.1%
2640 4
0.4%
2700 2
0.2%
2800 1
 
0.1%
2860 1
 
0.1%
ValueCountFrequency (%)
114920 1
0.1%
112320 1
0.1%
108480 1
0.1%
108030 1
0.1%
107900 1
0.1%
105820 1
0.1%
105040 1
0.1%
104610 1
0.1%
103560 1
0.1%
101860 1
0.1%

injury_claim
Real number (ℝ)

High correlation  Zeros 

Distinct638
Distinct (%)63.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7433.42
Minimum0
Maximum21450
Zeros25
Zeros (%)2.5%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2025-08-15T13:28:21.505847image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile450
Q14295
median6775
Q311305
95-th percentile15662
Maximum21450
Range21450
Interquartile range (IQR)7010

Descriptive statistics

Standard deviation4880.9519
Coefficient of variation (CV)0.65662264
Kurtosis-0.76308706
Mean7433.42
Median Absolute Deviation (MAD)3705
Skewness0.26481088
Sum7433420
Variance23823691
MonotonicityNot monotonic
2025-08-15T13:28:21.549804image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 25
 
2.5%
640 7
 
0.7%
480 7
 
0.7%
5540 5
 
0.5%
860 5
 
0.5%
660 5
 
0.5%
1180 5
 
0.5%
580 5
 
0.5%
13520 5
 
0.5%
6340 5
 
0.5%
Other values (628) 926
92.6%
ValueCountFrequency (%)
0 25
2.5%
10 1
 
0.1%
220 1
 
0.1%
250 1
 
0.1%
280 2
 
0.2%
290 1
 
0.1%
300 3
 
0.3%
330 2
 
0.2%
350 1
 
0.1%
360 1
 
0.1%
ValueCountFrequency (%)
21450 1
0.1%
21330 1
0.1%
20700 1
0.1%
19020 1
0.1%
18520 1
0.1%
18220 1
0.1%
18180 1
0.1%
18080 1
0.1%
18000 1
0.1%
17880 1
0.1%

property_claim
Real number (ℝ)

High correlation  Zeros 

Distinct626
Distinct (%)62.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7399.57
Minimum0
Maximum23670
Zeros19
Zeros (%)1.9%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2025-08-15T13:28:21.607050image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile450
Q14445
median6750
Q310885
95-th percentile15540
Maximum23670
Range23670
Interquartile range (IQR)6440

Descriptive statistics

Standard deviation4824.7262
Coefficient of variation (CV)0.65202791
Kurtosis-0.37638631
Mean7399.57
Median Absolute Deviation (MAD)3290
Skewness0.37816878
Sum7399570
Variance23277983
MonotonicityNot monotonic
2025-08-15T13:28:21.654917image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 19
 
1.9%
860 6
 
0.6%
11080 5
 
0.5%
480 5
 
0.5%
650 5
 
0.5%
640 5
 
0.5%
660 5
 
0.5%
10000 5
 
0.5%
6330 4
 
0.4%
5310 4
 
0.4%
Other values (616) 937
93.7%
ValueCountFrequency (%)
0 19
1.9%
20 1
 
0.1%
240 1
 
0.1%
250 1
 
0.1%
260 1
 
0.1%
280 3
 
0.3%
290 2
 
0.2%
300 3
 
0.3%
320 3
 
0.3%
330 1
 
0.1%
ValueCountFrequency (%)
23670 1
0.1%
21810 1
0.1%
21630 1
0.1%
21580 1
0.1%
21240 1
0.1%
20550 1
0.1%
20310 1
0.1%
20280 1
0.1%
19950 1
0.1%
19650 1
0.1%

vehicle_claim
Real number (ℝ)

High correlation 

Distinct726
Distinct (%)72.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37928.95
Minimum70
Maximum79560
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2025-08-15T13:28:21.710740image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum70
5-th percentile3273.5
Q130292.5
median42100
Q350822.5
95-th percentile63094.5
Maximum79560
Range79490
Interquartile range (IQR)20530

Descriptive statistics

Standard deviation18886.253
Coefficient of variation (CV)0.49793767
Kurtosis-0.44657292
Mean37928.95
Median Absolute Deviation (MAD)9840
Skewness-0.62109793
Sum37928950
Variance3.5669055 × 108
MonotonicityNot monotonic
2025-08-15T13:28:21.762788image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5040 7
 
0.7%
3360 6
 
0.6%
4720 5
 
0.5%
44800 5
 
0.5%
3600 5
 
0.5%
33600 5
 
0.5%
52080 5
 
0.5%
45360 4
 
0.4%
41580 4
 
0.4%
42720 4
 
0.4%
Other values (716) 950
95.0%
ValueCountFrequency (%)
70 1
0.1%
1440 2
0.2%
1680 2
0.2%
1750 1
0.1%
1760 1
0.1%
1800 1
0.1%
1960 2
0.2%
1980 1
0.1%
2030 1
0.1%
2080 1
0.1%
ValueCountFrequency (%)
79560 1
0.1%
77760 1
0.1%
77670 2
0.2%
76400 1
0.1%
76000 1
0.1%
75600 1
0.1%
75530 1
0.1%
74790 1
0.1%
73620 1
0.1%
73260 1
0.1%

auto_make
Categorical

High correlation 

Distinct14
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size53.5 KiB
Saab
80 
Dodge
80 
Suburu
80 
Nissan
78 
Chevrolet
76 
Other values (9)
606 

Length

Max length10
Median length9
Mean length5.703
Min length3

Characters and Unicode

Total characters5703
Distinct characters33
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSaab
2nd rowMercedes
3rd rowDodge
4th rowChevrolet
5th rowAccura

Common Values

ValueCountFrequency (%)
Saab 80
 
8.0%
Dodge 80
 
8.0%
Suburu 80
 
8.0%
Nissan 78
 
7.8%
Chevrolet 76
 
7.6%
Ford 72
 
7.2%
BMW 72
 
7.2%
Toyota 70
 
7.0%
Audi 69
 
6.9%
Accura 68
 
6.8%
Other values (4) 255
25.5%

Length

2025-08-15T13:28:21.819823image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
saab 80
 
8.0%
dodge 80
 
8.0%
suburu 80
 
8.0%
nissan 78
 
7.8%
chevrolet 76
 
7.6%
ford 72
 
7.2%
bmw 72
 
7.2%
toyota 70
 
7.0%
audi 69
 
6.9%
accura 68
 
6.8%
Other values (4) 255
25.5%

Most occurring characters

ValueCountFrequency (%)
e 629
 
11.0%
a 499
 
8.7%
o 491
 
8.6%
u 377
 
6.6%
r 361
 
6.3%
d 341
 
6.0%
s 289
 
5.1%
c 201
 
3.5%
n 201
 
3.5%
S 160
 
2.8%
Other values (23) 2154
37.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5703
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 629
 
11.0%
a 499
 
8.7%
o 491
 
8.6%
u 377
 
6.6%
r 361
 
6.3%
d 341
 
6.0%
s 289
 
5.1%
c 201
 
3.5%
n 201
 
3.5%
S 160
 
2.8%
Other values (23) 2154
37.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5703
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 629
 
11.0%
a 499
 
8.7%
o 491
 
8.6%
u 377
 
6.6%
r 361
 
6.3%
d 341
 
6.0%
s 289
 
5.1%
c 201
 
3.5%
n 201
 
3.5%
S 160
 
2.8%
Other values (23) 2154
37.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5703
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 629
 
11.0%
a 499
 
8.7%
o 491
 
8.6%
u 377
 
6.6%
r 361
 
6.3%
d 341
 
6.0%
s 289
 
5.1%
c 201
 
3.5%
n 201
 
3.5%
S 160
 
2.8%
Other values (23) 2154
37.8%

auto_model
Categorical

High correlation 

Distinct39
Distinct (%)3.9%
Missing0
Missing (%)0.0%
Memory size53.0 KiB
RAM
 
43
Wrangler
 
42
A3
 
37
Neon
 
37
MDX
 
36
Other values (34)
805 

Length

Max length14
Median length9
Mean length5.178
Min length2

Characters and Unicode

Total characters5178
Distinct characters52
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row92x
2nd rowE400
3rd rowRAM
4th rowTahoe
5th rowRSX

Common Values

ValueCountFrequency (%)
RAM 43
 
4.3%
Wrangler 42
 
4.2%
A3 37
 
3.7%
Neon 37
 
3.7%
MDX 36
 
3.6%
Jetta 35
 
3.5%
Passat 33
 
3.3%
Legacy 32
 
3.2%
A5 32
 
3.2%
Pathfinder 31
 
3.1%
Other values (29) 642
64.2%

Length

2025-08-15T13:28:21.861668image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ram 43
 
4.1%
wrangler 42
 
4.0%
a3 37
 
3.5%
neon 37
 
3.5%
mdx 36
 
3.5%
jetta 35
 
3.4%
passat 33
 
3.2%
legacy 32
 
3.1%
a5 32
 
3.1%
pathfinder 31
 
3.0%
Other values (31) 685
65.7%

Most occurring characters

ValueCountFrequency (%)
a 492
 
9.5%
e 428
 
8.3%
r 392
 
7.6%
o 238
 
4.6%
i 235
 
4.5%
t 185
 
3.6%
l 179
 
3.5%
n 178
 
3.4%
M 168
 
3.2%
s 157
 
3.0%
Other values (42) 2526
48.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5178
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 492
 
9.5%
e 428
 
8.3%
r 392
 
7.6%
o 238
 
4.6%
i 235
 
4.5%
t 185
 
3.6%
l 179
 
3.5%
n 178
 
3.4%
M 168
 
3.2%
s 157
 
3.0%
Other values (42) 2526
48.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5178
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 492
 
9.5%
e 428
 
8.3%
r 392
 
7.6%
o 238
 
4.6%
i 235
 
4.5%
t 185
 
3.6%
l 179
 
3.5%
n 178
 
3.4%
M 168
 
3.2%
s 157
 
3.0%
Other values (42) 2526
48.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5178
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 492
 
9.5%
e 428
 
8.3%
r 392
 
7.6%
o 238
 
4.6%
i 235
 
4.5%
t 185
 
3.6%
l 179
 
3.5%
n 178
 
3.4%
M 168
 
3.2%
s 157
 
3.0%
Other values (42) 2526
48.8%

auto_year
Real number (ℝ)

Distinct21
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2005.103
Minimum1995
Maximum2015
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2025-08-15T13:28:21.908883image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1995
5-th percentile1995
Q12000
median2005
Q32010
95-th percentile2014
Maximum2015
Range20
Interquartile range (IQR)10

Descriptive statistics

Standard deviation6.0158608
Coefficient of variation (CV)0.0030002752
Kurtosis-1.1718678
Mean2005.103
Median Absolute Deviation (MAD)5
Skewness-0.048288807
Sum2005103
Variance36.190582
MonotonicityNot monotonic
2025-08-15T13:28:21.951928image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
1995 56
 
5.6%
1999 55
 
5.5%
2005 54
 
5.4%
2006 53
 
5.3%
2011 53
 
5.3%
2007 52
 
5.2%
2003 51
 
5.1%
2009 50
 
5.0%
2010 50
 
5.0%
2013 49
 
4.9%
Other values (11) 477
47.7%
ValueCountFrequency (%)
1995 56
5.6%
1996 37
3.7%
1997 46
4.6%
1998 40
4.0%
1999 55
5.5%
2000 42
4.2%
2001 42
4.2%
2002 49
4.9%
2003 51
5.1%
2004 39
3.9%
ValueCountFrequency (%)
2015 47
4.7%
2014 44
4.4%
2013 49
4.9%
2012 46
4.6%
2011 53
5.3%
2010 50
5.0%
2009 50
5.0%
2008 45
4.5%
2007 52
5.2%
2006 53
5.3%

fraud_reported
Boolean

High correlation 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
False
753 
True
247 
ValueCountFrequency (%)
False 753
75.3%
True 247
 
24.7%
2025-08-15T13:28:21.987290image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

_c39
Unsupported

Missing  Rejected  Unsupported 

Missing1000
Missing (%)100.0%
Memory size7.9 KiB

Interactions

2025-08-15T13:28:17.151148image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-15T13:28:04.084738image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-15T13:28:04.829572image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-15T13:28:05.452363image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-15T13:28:06.119494image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-15T13:28:06.920682image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-15T13:28:08.065245image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-15T13:28:10.617289image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-15T13:28:12.841828image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-15T13:28:13.698865image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-15T13:28:14.346133image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-15T13:28:15.043174image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-15T13:28:15.814183image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-15T13:28:16.451190image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-15T13:28:17.199803image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-15T13:28:04.132127image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-15T13:28:04.869751image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-15T13:28:05.499507image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-15T13:28:06.169508image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-15T13:28:06.965246image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-15T13:28:08.246783image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-15T13:28:10.753252image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-15T13:28:12.999062image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-15T13:28:13.742002image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-15T13:28:14.394604image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-15T13:28:15.091736image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-15T13:28:15.861957image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-15T13:28:16.500341image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-15T13:28:17.249822image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-15T13:28:04.180792image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-15T13:28:04.910537image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-15T13:28:05.540708image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-15T13:28:06.218738image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-15T13:28:07.008502image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-15T13:28:08.398526image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-15T13:28:10.899620image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-15T13:28:13.040901image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-15T13:28:13.787274image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-15T13:28:14.441139image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-15T13:28:15.132274image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-15T13:28:15.904338image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-15T13:28:16.545978image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-15T13:28:17.301152image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-15T13:28:04.225561image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-15T13:28:04.949461image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-15T13:28:05.589350image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-15T13:28:06.264558image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-15T13:28:07.053650image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-15T13:28:08.573332image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-15T13:28:11.050454image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-15T13:28:13.084436image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-15T13:28:13.832224image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-15T13:28:14.483860image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-15T13:28:15.179893image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-15T13:28:15.944823image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-15T13:28:16.594602image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-15T13:28:17.352455image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-15T13:28:04.269529image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-15T13:28:05.000794image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-15T13:28:05.634315image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-15T13:28:06.318545image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-15T13:28:07.104845image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-15T13:28:08.804531image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-15T13:28:11.192384image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-15T13:28:13.233975image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-15T13:28:13.880981image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-15T13:28:14.533744image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-15T13:28:15.230937image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-15T13:28:15.994126image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-15T13:28:16.647657image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-15T13:28:17.409346image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-15T13:28:04.320622image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-15T13:28:05.046469image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-15T13:28:05.685049image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-15T13:28:06.367070image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-15T13:28:07.148605image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-15T13:28:08.966590image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-15T13:28:11.296082image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-15T13:28:13.280392image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-15T13:28:13.926350image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-15T13:28:14.584419image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-15T13:28:15.276402image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-15T13:28:16.030505image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-15T13:28:16.701321image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-15T13:28:17.464604image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-15T13:28:04.369526image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-15T13:28:05.097523image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-15T13:28:05.737326image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-15T13:28:06.419660image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-15T13:28:07.199721image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-15T13:28:09.143438image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-15T13:28:11.451985image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-15T13:28:13.329714image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-15T13:28:13.971398image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-15T13:28:14.637206image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-15T13:28:15.327255image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-15T13:28:16.083845image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-15T13:28:16.752550image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-15T13:28:17.512125image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-15T13:28:04.419453image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-15T13:28:05.139788image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-15T13:28:05.784872image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-15T13:28:06.469806image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-15T13:28:07.247392image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-15T13:28:09.325689image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-15T13:28:11.592035image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-15T13:28:13.373573image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-15T13:28:14.019687image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-15T13:28:14.683738image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-15T13:28:15.379528image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-15T13:28:16.129501image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-15T13:28:16.800732image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-15T13:28:17.561420image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-15T13:28:04.464598image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-15T13:28:05.184490image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-15T13:28:05.842502image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-15T13:28:06.520744image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-15T13:28:07.290531image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-15T13:28:09.507540image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-15T13:28:11.756849image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-15T13:28:13.422573image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-15T13:28:14.063926image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-15T13:28:14.730650image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-15T13:28:15.429924image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-15T13:28:16.171319image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-15T13:28:16.850712image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-15T13:28:17.616414image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-15T13:28:04.510776image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-15T13:28:05.231060image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-15T13:28:05.885066image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-15T13:28:06.569665image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-15T13:28:07.336745image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-15T13:28:09.673562image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-15T13:28:11.923383image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-15T13:28:13.466690image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-15T13:28:14.116683image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-15T13:28:14.782422image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-15T13:28:15.470826image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-15T13:28:16.213386image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-15T13:28:16.898789image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-15T13:28:17.668583image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-15T13:28:04.556728image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-15T13:28:05.276901image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-15T13:28:05.930441image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-15T13:28:06.623932image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-15T13:28:07.394811image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-15T13:28:09.896309image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-15T13:28:12.133145image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-15T13:28:13.514109image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-15T13:28:14.165312image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-15T13:28:14.830985image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-15T13:28:15.520255image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-15T13:28:16.263562image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-15T13:28:16.948677image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-15T13:28:17.714694image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-15T13:28:04.603923image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-15T13:28:05.319697image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-15T13:28:05.973425image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-15T13:28:06.670389image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-15T13:28:07.582358image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-15T13:28:10.069876image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-15T13:28:12.285190image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-15T13:28:13.557092image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-15T13:28:14.207344image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-15T13:28:14.881323image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-15T13:28:15.560757image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-15T13:28:16.299527image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-15T13:28:16.998502image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-15T13:28:17.761914image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-15T13:28:04.647176image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-15T13:28:05.359433image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-15T13:28:06.019976image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-15T13:28:06.721704image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-15T13:28:07.737786image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-15T13:28:10.252187image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-15T13:28:12.443921image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-15T13:28:13.598779image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-15T13:28:14.249483image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-15T13:28:14.930250image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-15T13:28:15.603736image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-15T13:28:16.345094image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-15T13:28:17.040912image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-15T13:28:17.917859image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-15T13:28:04.778608image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-15T13:28:05.402087image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-15T13:28:06.070723image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-15T13:28:06.779546image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-15T13:28:07.933609image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-15T13:28:10.437586image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-15T13:28:12.643119image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-15T13:28:13.645792image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-15T13:28:14.298635image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-15T13:28:14.986846image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-15T13:28:15.761925image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-15T13:28:16.403467image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-15T13:28:17.096755image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-08-15T13:28:22.039332image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ageauthorities_contactedauto_makeauto_modelauto_yearbodily_injuriescapital-gainscapital-losscollision_typefraud_reportedincident_cityincident_hour_of_the_dayincident_severityincident_stateincident_typeinjury_claiminsured_education_levelinsured_hobbiesinsured_occupationinsured_relationshipinsured_sexinsured_zipmonths_as_customernumber_of_vehicles_involvedpolice_report_availablepolicy_annual_premiumpolicy_cslpolicy_deductablepolicy_numberpolicy_stateproperty_claimproperty_damagetotal_claim_amountumbrella_limitvehicle_claimwitnesses
age1.0000.0000.0000.0000.0060.000-0.0210.0010.0660.0000.0000.0950.0530.0000.0000.0740.0000.0000.0000.0370.0800.0090.9130.0810.0000.0310.0000.0000.0610.0000.0560.0000.0650.0020.0510.046
authorities_contacted0.0001.0000.0000.0000.0770.0000.0000.0270.2720.0760.0400.1350.2120.0000.2680.2590.0000.0000.0000.0470.0480.0000.0000.0700.0000.0270.0270.0200.0000.0000.2530.0000.2810.0000.2680.000
auto_make0.0000.0001.0000.9870.0000.0000.0000.0130.0000.0280.0000.0000.0000.0270.0000.0000.0380.0470.0000.0120.0000.0000.0000.0410.0000.0000.0000.0000.0260.0500.0300.0000.0000.0060.0000.000
auto_model0.0000.0000.9871.0000.0000.0000.0000.0000.0000.0930.0640.0000.0310.0140.0890.0000.0280.0510.0370.0230.0000.0000.0000.0870.0350.0000.0000.0000.0000.0500.1010.0000.0140.0300.0000.000
auto_year0.0060.0770.0000.0001.0000.0440.027-0.0550.0380.0000.0000.0200.0000.0490.048-0.0190.0000.0000.0380.0600.044-0.0280.0040.0330.047-0.0300.0000.000-0.0010.031-0.0080.000-0.0330.012-0.0410.018
bodily_injuries0.0000.0000.0000.0000.0441.0000.0530.0000.0000.0000.0000.0240.0000.0000.0100.0000.0090.0000.0940.0000.0000.0000.0440.0000.0000.0000.0000.0180.0000.0470.0000.0170.0000.0680.0000.017
capital-gains-0.0210.0000.0000.0000.0270.0531.000-0.0410.0000.0000.000-0.0160.0000.0000.0000.0230.0490.0610.0000.0000.0000.015-0.0050.0770.000-0.0150.0000.0000.0070.0000.0050.0000.012-0.0430.0100.000
capital-loss0.0010.0270.0130.000-0.0550.000-0.0411.0000.0380.0000.000-0.0290.0000.0000.000-0.0460.0280.0070.0000.0410.0280.0420.0140.0000.0420.0320.0000.000-0.0080.000-0.0230.058-0.042-0.021-0.0400.000
collision_type0.0660.2720.0000.0000.0380.0000.0000.0381.0000.1680.0300.2760.4250.0610.5770.5330.0560.0490.0000.0000.0000.0000.0320.2280.0130.0000.0470.0000.0460.0230.5440.0000.5770.0000.5750.053
fraud_reported0.0000.0760.0280.0930.0000.0000.0000.0000.1681.0000.0000.1090.5110.1010.1620.1140.0000.3790.0680.0200.0000.0670.0000.0300.0000.0130.0120.0000.0000.0000.1650.0780.1580.0420.1690.056
incident_city0.0000.0400.0000.0640.0000.0000.0000.0000.0300.0001.0000.0000.0000.0000.0310.0000.0000.0290.0000.0000.0100.0310.0290.0000.0000.0000.0000.0000.0260.0230.0190.0650.0000.0680.0000.053
incident_hour_of_the_day0.0950.1350.0000.0000.0200.024-0.016-0.0290.2760.1090.0001.0000.1950.0350.2640.1660.0000.0000.0000.0000.0000.0100.0760.1140.000-0.0030.0000.074-0.0010.0290.1690.0000.178-0.0210.1740.054
incident_severity0.0530.2120.0000.0310.0000.0000.0000.0000.4250.5110.0000.1951.0000.0290.4250.3900.0000.0000.0000.0000.0000.0220.0540.1710.0100.0000.0070.0000.0000.0000.3970.0620.4280.0540.4270.020
incident_state0.0000.0000.0270.0140.0490.0000.0000.0000.0610.1010.0000.0350.0291.0000.0240.0000.0440.0690.0290.0290.0640.0270.0000.0270.0270.0000.0000.0210.0000.0000.0000.0000.0000.0210.0340.030
incident_type0.0000.2680.0000.0890.0480.0100.0000.0000.5770.1620.0310.2640.4250.0241.0000.5330.0290.0380.0110.0000.0000.0180.0000.5760.0000.0000.0270.0000.0000.0000.5400.0000.5760.0000.5770.000
injury_claim0.0740.2590.0000.000-0.0190.0000.023-0.0460.5330.1140.0000.1660.3900.0000.5331.0000.0000.0370.0330.0420.000-0.0080.0640.2010.033-0.0190.0000.053-0.0110.0610.5690.0000.792-0.0470.6840.000
insured_education_level0.0000.0000.0380.0280.0000.0090.0490.0280.0560.0000.0000.0000.0000.0440.0290.0001.0000.0000.0410.0420.0000.0000.0000.0200.0500.0000.0640.0000.0000.0360.0000.0210.0540.0000.0450.060
insured_hobbies0.0000.0000.0470.0510.0000.0000.0610.0070.0490.3790.0290.0000.0000.0690.0380.0370.0001.0000.0500.0270.0000.0120.0290.0000.0940.0000.0000.0000.0480.0410.0340.0000.0150.0000.0360.034
insured_occupation0.0000.0000.0000.0370.0380.0940.0000.0000.0000.0680.0000.0000.0000.0290.0110.0330.0410.0501.0000.0540.0000.0770.0000.0000.0000.0350.0510.0810.0140.0000.0000.0000.0360.0270.0540.031
insured_relationship0.0370.0470.0120.0230.0600.0000.0000.0410.0000.0200.0000.0000.0000.0290.0000.0420.0420.0270.0541.0000.0000.0000.0640.0000.0000.0000.0380.0000.0520.0000.0000.0000.0000.0430.0000.000
insured_sex0.0800.0480.0000.0000.0440.0000.0000.0280.0000.0000.0100.0000.0000.0640.0000.0000.0000.0000.0000.0001.0000.0400.0340.0000.0000.1190.0600.0000.0000.0000.0250.0000.0370.0000.0000.000
insured_zip0.0090.0000.0000.000-0.0280.0000.0150.0420.0000.0670.0310.0100.0220.0270.018-0.0080.0000.0120.0770.0000.0401.0000.0130.0330.0680.0430.0000.000-0.0010.014-0.0150.037-0.0030.004-0.0160.000
months_as_customer0.9130.0000.0000.0000.0040.044-0.0050.0140.0320.0000.0290.0760.0540.0000.0000.0640.0000.0290.0000.0640.0340.0131.0000.0000.0920.0200.0000.0000.0580.0170.0250.0000.0530.0050.0480.000
number_of_vehicles_involved0.0810.0700.0410.0870.0330.0000.0770.0000.2280.0300.0000.1140.1710.0270.5760.2010.0200.0000.0000.0000.0000.0330.0001.0000.0000.0000.0000.0480.0000.0000.2050.0000.2340.0300.2340.000
police_report_available0.0000.0000.0000.0350.0470.0000.0000.0420.0130.0000.0000.0000.0100.0270.0000.0330.0500.0940.0000.0000.0000.0680.0920.0001.0000.0340.0330.0000.0000.0380.0000.0260.0730.0680.0650.000
policy_annual_premium0.0310.0270.0000.000-0.0300.000-0.0150.0320.0000.0130.000-0.0030.0000.0000.000-0.0190.0000.0000.0350.0000.1190.0430.0200.0000.0341.0000.0790.0520.0180.000-0.0040.056-0.002-0.0010.0070.036
policy_csl0.0000.0270.0000.0000.0000.0000.0000.0000.0470.0120.0000.0000.0070.0000.0270.0000.0640.0000.0510.0380.0600.0000.0000.0000.0330.0791.0000.0000.0000.0000.0000.0000.0000.0000.0280.020
policy_deductable0.0000.0200.0000.0000.0000.0180.0000.0000.0000.0000.0000.0740.0000.0210.0000.0530.0000.0000.0810.0000.0000.0000.0000.0480.0000.0520.0001.0000.0000.0000.0630.0000.0460.0000.0000.043
policy_number0.0610.0000.0260.000-0.0010.0000.007-0.0080.0460.0000.026-0.0010.0000.0000.000-0.0110.0000.0480.0140.0520.000-0.0010.0580.0000.0000.0180.0000.0001.0000.058-0.0030.056-0.0080.005-0.0130.041
policy_state0.0000.0000.0500.0500.0310.0470.0000.0000.0230.0000.0230.0290.0000.0000.0000.0610.0360.0410.0000.0000.0000.0140.0170.0000.0380.0000.0000.0000.0581.0000.0000.0370.0240.0000.0000.000
property_claim0.0560.2530.0300.101-0.0080.0000.005-0.0230.5440.1650.0190.1690.3970.0000.5400.5690.0000.0340.0000.0000.025-0.0150.0250.2050.000-0.0040.0000.063-0.0030.0001.0000.0270.798-0.0180.6930.000
property_damage0.0000.0000.0000.0000.0000.0170.0000.0580.0000.0780.0650.0000.0620.0000.0000.0000.0210.0000.0000.0000.0000.0370.0000.0000.0260.0560.0000.0000.0560.0370.0271.0000.0000.0430.0000.000
total_claim_amount0.0650.2810.0000.014-0.0330.0000.012-0.0420.5770.1580.0000.1780.4280.0000.5760.7920.0540.0150.0360.0000.037-0.0030.0530.2340.073-0.0020.0000.046-0.0080.0240.7980.0001.000-0.0410.9650.000
umbrella_limit0.0020.0000.0060.0300.0120.068-0.043-0.0210.0000.0420.068-0.0210.0540.0210.000-0.0470.0000.0000.0270.0430.0000.0040.0050.0300.068-0.0010.0000.0000.0050.000-0.0180.043-0.0411.000-0.0380.000
vehicle_claim0.0510.2680.0000.000-0.0410.0000.010-0.0400.5750.1690.0000.1740.4270.0340.5770.6840.0450.0360.0540.0000.000-0.0160.0480.2340.0650.0070.0280.000-0.0130.0000.6930.0000.965-0.0381.0000.000
witnesses0.0460.0000.0000.0000.0180.0170.0000.0000.0530.0560.0530.0540.0200.0300.0000.0000.0600.0340.0310.0000.0000.0000.0000.0000.0000.0360.0200.0430.0410.0000.0000.0000.0000.0000.0001.000

Missing values

2025-08-15T13:28:18.020489image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-08-15T13:28:18.159465image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

months_as_customeragepolicy_numberpolicy_bind_datepolicy_statepolicy_cslpolicy_deductablepolicy_annual_premiumumbrella_limitinsured_zipinsured_sexinsured_education_levelinsured_occupationinsured_hobbiesinsured_relationshipcapital-gainscapital-lossincident_dateincident_typecollision_typeincident_severityauthorities_contactedincident_stateincident_cityincident_locationincident_hour_of_the_daynumber_of_vehicles_involvedproperty_damagebodily_injurieswitnessespolice_report_availabletotal_claim_amountinjury_claimproperty_claimvehicle_claimauto_makeauto_modelauto_yearfraud_reported_c39
0328485215852014-10-17OH250/50010001406.910466132MALEMDcraft-repairsleepinghusband5330002015-01-25Single Vehicle CollisionSide CollisionMajor DamagePoliceSCColumbus9935 4th Drive51YES12YES7161065101302052080Saab92x2004YNaN
1228423428682006-06-27IN250/50020001197.225000000468176MALEMDmachine-op-inspctreadingother-relative002015-01-21Vehicle Theft?Minor DamagePoliceVARiverwood6608 MLK Hwy81?00?50707807803510MercedesE4002007YNaN
2134296876982000-09-06OH100/30020001413.145000000430632FEMALEPhDsalesboard-gamesown-child3510002015-02-22Multi-vehicle CollisionRear CollisionMinor DamagePoliceNYColumbus7121 Francis Lane73NO23NO346507700385023100DodgeRAM2007NNaN
3256412278111990-05-25IL250/50020001415.746000000608117FEMALEPhDarmed-forcesboard-gamesunmarried48900-624002015-01-10Single Vehicle CollisionFront CollisionMajor DamagePoliceOHArlington6956 Maple Drive51?12NO634006340634050720ChevroletTahoe2014YNaN
4228443674552014-06-06IL500/100010001583.916000000610706MALEAssociatesalesboard-gamesunmarried66000-460002015-02-17Vehicle Theft?Minor DamageNaNNYArlington3041 3rd Ave201NO01NO650013006504550AccuraRSX2009NNaN
5256391045942006-10-12OH250/50010001351.100478456FEMALEPhDtech-supportbungie-jumpingunmarried002015-01-02Multi-vehicle CollisionRear CollisionMajor DamageFireSCArlington8973 Washington St193NO02NO641006410641051280Saab952003YNaN
6137344139782000-06-04IN250/50010001333.350441716MALEPhDprof-specialtyboard-gameshusband0-770002015-01-13Multi-vehicle CollisionFront CollisionMinor DamagePoliceNYSpringfield5846 Weaver Drive03?00?7865021450715050050NissanPathfinder2012NNaN
7165374290271990-02-03IL100/30010001137.030603195MALEAssociatetech-supportbase-jumpingunmarried002015-02-27Multi-vehicle CollisionFront CollisionTotal LossPoliceVAColumbus3525 3rd Hwy233?22YES515909380938032830AudiA52015NNaN
827334856651997-02-05IL100/3005001442.990601734FEMALEPhDother-servicegolfown-child002015-01-30Single Vehicle CollisionFront CollisionTotal LossPoliceWVArlington4872 Rock Ridge211NO11YES277002770277022160ToyotaCamry2012NNaN
9212426365502011-07-25IL100/3005001315.680600983MALEPhDpriv-house-servcampingwife0-393002015-01-05Single Vehicle CollisionRear CollisionTotal LossOtherNCHillsdale3066 Francis Ave141NO21?423004700470032900Saab92x1996NNaN
months_as_customeragepolicy_numberpolicy_bind_datepolicy_statepolicy_cslpolicy_deductablepolicy_annual_premiumumbrella_limitinsured_zipinsured_sexinsured_education_levelinsured_occupationinsured_hobbiesinsured_relationshipcapital-gainscapital-lossincident_dateincident_typecollision_typeincident_severityauthorities_contactedincident_stateincident_cityincident_locationincident_hour_of_the_daynumber_of_vehicles_involvedproperty_damagebodily_injurieswitnessespolice_report_availabletotal_claim_amountinjury_claimproperty_claimvehicle_claimauto_makeauto_modelauto_yearfraud_reported_c39
990286436631901994-02-05IL100/3005001564.433000000477644FEMALEMDprof-specialtymoviesunmarried77500-328002015-01-31Single Vehicle CollisionRear CollisionMinor DamageFireNYNorthbrook4755 1st St181?22YES342903810381026670JeepGrand Cherokee2013NNaN
991257441093922006-07-12OH100/30010001280.880433981MALEMDother-servicebasketballother-relative59400-322002015-02-06Single Vehicle CollisionRear CollisionTotal LossOtherWVRiverwood5312 Francis Ridge211NO01NO469800522041760AccuraTL2002NNaN
99294262152782007-10-24IN100/300500722.660433696MALEMDexec-managerialcampinghusband5030002015-01-23Multi-vehicle CollisionFront CollisionMajor DamageFireOHSpringfield1705 Weaver St63YES12YES367003670734025690NissanPathfinder2010NNaN
993124286745702001-12-08OH250/50010001235.140443567MALEMDexec-managerialcampinghusband0-321002015-02-17Multi-vehicle CollisionSide CollisionTotal LossOtherOHHillsdale1643 Washington Hwy203?01?602006020602048160VolkswagenPassat2012NNaN
994141306814862007-03-24IN500/100010001347.040430665MALEHigh Schoolsalesbungie-jumpingown-child0-821002015-01-22Parked Car?Minor DamageNaNSCNorthbend6516 Solo Drive61?12YES648054010804860HondaCivic1996NNaN
9953389418511991-07-16OH500/100010001310.800431289FEMALEMasterscraft-repairpaintballunmarried002015-02-22Single Vehicle CollisionFront CollisionMinor DamageFireNCNorthbrook6045 Andromedia St201YES01?8720017440872061040HondaAccord2006NNaN
996285411869342014-01-05IL100/30010001436.790608177FEMALEPhDprof-specialtysleepingwife7090002015-01-24Single Vehicle CollisionRear CollisionMajor DamageFireSCNorthbend3092 Texas Drive231YES23?108480180801808072320VolkswagenPassat2015NNaN
997130349185162003-02-17OH250/5005001383.493000000442797FEMALEMastersarmed-forcesbungie-jumpingother-relative3510002015-01-23Multi-vehicle CollisionSide CollisionMinor DamagePoliceNCArlington7629 5th St43?23YES675007500750052500SuburuImpreza1996NNaN
998458625339402011-11-18IL500/100020001356.925000000441714MALEAssociatehandlers-cleanersbase-jumpingwife002015-02-26Single Vehicle CollisionRear CollisionMajor DamageOtherNYArlington6128 Elm Lane21?01YES469805220522036540AudiA51998NNaN
999456605560801996-11-11OH250/5001000766.190612260FEMALEAssociatesaleskayakinghusband002015-02-26Parked Car?Minor DamagePoliceWVColumbus1416 Cherokee Ridge61?03?50604609203680MercedesE4002007NNaN